Advanced 3-D interpretation tools based on imaging, Bayesian inversion and artificial neural<br>network (ANN) recognition developed by the author (Spichak et al., 1999; Spichak, 2007)<br>form a basement of a new paradigm in the electromagnetic data interpretation that takes into<br>account the geological information known, noise level in the data, prior estimates of the<br>unknown parameters, hypotheses formulated in probabilistic terms, data available from other<br>methods and formalized expert estimates. Application of these methods to magnetotelluric<br>sounding data enables constructing 3-D electrical resistivity models of the geothermal areas,<br>mapping the geothermal reservoirs and monitoring macro-parameters of the fluid bearing<br>faults.<br>In particular, Spichak (2002) used Bayesian inversion of MT data in order to construct 3-D<br>resistivity model of the Minamikayabe geothermal area (Hokkaido, Japan). Spichak (2001)<br>has found the most suitable data transforms for adequate interpretation of MT measurements<br>carried out with the purpose of monitoring variations in the geothermal reservoir resistivity<br>with temperature. Finally, using ANN Expert System enabled to estimate the Minou fault<br>(Kyushu, Japan) macro-parameters (Spichak et al., 2002).


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